On Efficient Clustering of Wireless Sensor Networks (original) (raw)
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Clustering in Wireless Sensor Networks
2009
The use of wireless sensor networks (WSNs) has grown enormously in the last decade, pointing out the crucial need for scalable and energy-efficient routing and data gathering and aggregation protocols in corresponding large-scale environments. Hierarchical clustering protocols (as opposed to direct single-tier communication schemes) have extensively been used toward the above directions. Moreover, they can greatly contribute to overall system scalability, lifetime, and energy efficiency.
Design and Implementation Issues of Clustering in Wireless Sensor Networks
International Journal of Computer Applications, 2012
Sensors have become a very trendy research area during the last few years covering a wide range of applications such as habitat monitoring, military surveillance, information collecting etc...Sensors used for these purposes needs to be deployed very densely and in a random fashion. They should be able to operate without human intervention. Clustering is a technique employed to increase the various capabilities of a sensor network. In this paper we discuss about the design and implementation issues of clustering algorithms employed in sensor networks.
Clustering objectives in wireless sensor networks: A survey and research direction analysis
Computer Networks, 2020
Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of networks given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.
Some Issues in Clustering Algorithms for Wireless Sensor Networks
Wireless Sensor Networks (WSNs) present new generation of real time embedded systems with limited computation, energy and memory resources that are being used in wide variety of applications where traditional networking infrastructure is practically infeasible. In recent years many approaches and techniques have been proposed for optimization of energy usage in Wireless Sensor Networks. In order to gather information more efficiently, wireless sensor networks are partitioned into clusters. However, these methods are not without problems. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop wireless sensor networks.
The large-scale deployment of wireless sensor networks (WSNs) and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy. In this article, we highlight the challenges in clustering a WSN, discuss the design rationale of the different clustering approaches, and classify the proposed approaches based on their objectives and design principles. We further discuss several key issues that affect the practical deployment of clustering techniques in sensor network applications.
Node Clustering for Wireless Sensor Networks
publications.muet.edu.pk
Recent years have witnessed considerable growth in the development and deployment of clustering methods which are not only used to maintain network resources but also increases the reliability of the WSNs (Wireless Sensor Network) and the facts manifest by the wide range of clustering solutions. Node clustering by selecting key parameters to tackle the dynamic behaviour of resource constraint WSN is a challenging issue. This paper highlights the recent progress which has been carried out pertaining to the development of clustering solutions for the WSNs. The paper presents classification of node clustering methods and their comparison based on the objectives, clustering criteria and methodology. In addition, the potential open issues which need to be considered for future work are high lighted.
Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network orientation, WSN can be of two types: flat network and hierarchical or cluster-based network. Various advantages of cluster-based WSN are energy efficiency, better network communication, efficient topology management, minimized delay, and so forth. Consequently, clustering has become a key research area in WSN. Different approaches for WSN, using cluster concepts, have been proposed. The objective of this paper is to review and analyze the latest prominent cluster-based WSN algorithms using various measurement parameters. In this paper, unique performance metrics are designed which efficiently evaluate prominent clustering schemes. Moreover, we also develop taxonomy for the classification of the clustering schemes. Based on performance metrics, quantitative and qualitative analyses are performed to compare the advantages and disadvantages of the algorithms. Finally, we also put forward open research issues in the development of low cost, scalable, robust clustering schemes.
Survey on Recent Clustering Algorithms in Wireless Sensor Networks
2013
The use of wireless sensor networks (WSNs) has grown enormously in the last decade, pointing out the crucial need for scalable and energy-efficient routing and data gathering and aggregation protocols in corresponding large-scale environments. To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. The objective of this paper is to present a survey on clustering algorithms reported in the literature of WSNs. This paper presents taxonomy of energy efficient clustering algorithms in WSNs.
On clustering in sensor networks
Why to build clusters in sensor networks ? Agregating nodes in clusters allows to reduce the complexity of the routing algorithms, to optimize the medium resource by letting it to be locally managed by a cluster head, to make easy the data fusion, to simplify the network management and particularly the address allocation, to optimize the energy consumption, and at last to make the network more scalable. Using clusters allows also to stabilize the topology if the cluster size is large in comparison to the speed of the nodes. This chapter is dedicated to clustering in sensor networks. First, the state of the art is presented, followed by the detailed presentation of one of the best and most cited cluster formation method with its validation and correction. Then, the next parts of the chapter are dedicated to some considerations on cluster modelling. In the last part, a method to assign addresses to the nodes within a cluster is presented.
Applying Clustering Strategies to Improve the Efficiency of Network in Wireless Sensor Networks
2019
Wireless Sensor Networks are comprised of thousands of sensor nodes which are disseminated in a specific region to screen natural conditions like temperature, sound, pressure and so on and agreeably pass their information to the base station. WSN is steadily creating innovation. There are substantial scale applications in WSN like ecological observing, front line mindfulness, temperature detecting and so on in this way, there is need of expanding network lifetime in WSN as changing sensors regularly isn't conceivable for all intents and purposes constantly. In the past methods, the clustering of nodes isn't balanced and this can make the network energy unbalanced. Based on their separation and location, making it basically not quite the same as the Proposed Location Based Clustering Algorithm (LBC) can perform superior to anything leaving LEACH and Rescue Phase to shape a cluster. In LBC algorithm the location of every single present hub in the network are computed as for X, Y-organizes. This can maintain a strategic distance from arbitrary choice of nodes in clusters. It enhances the adjusting of the network and energy of network can be spared. Proposed Center Point Detection Clustering Algorithm (CPDC) decides the focal point of the cluster and closest hub to that point with high energy chose as Cluster Head (CH).